利用麻雀搜索算法优化深度置信网络的入侵检测研究

Intrusion Detection Research for Deep Belief Network Optimized by Sparrow Search Algorithm

针对人工经验设定深度置信网络的网络结构致使其很难达到最优,进而导致深度置信网络的性能无法完全发挥的问题,提出利用麻雀搜索算法优化深度置信网络的入侵检测模型。实验结果表明:相比未优化的深度置信网络,性能有显著提升,该模型优异的性能有效提高了入侵检测识别的效率。

Aiming at the fact that the network structure of deep belief network(DBN)based on artificial experience failed to be optimal and it troubled full play of the performance of the deep confidence network,making use of sparrow search algorithm to optimize DBN’s intrusion detection model was proposed.The experimental results show that,compared with the non-optimized DBN,the final model’s performance is excellent and it can effectively improve the efficiency of intrusion detection.